{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5586a8be",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/datamongerbonny/PythonGeo_notebooks/blob/main/Geospatial%20in%20the%20cloud.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75e0b53a",
   "metadata": {
    "id": "75e0b53a"
   },
   "source": [
    "# Chapter 4: Geospatial in the Cloud"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "SGYfPSRi_Z59",
   "metadata": {
    "id": "SGYfPSRi_Z59"
   },
   "source": [
    "\n",
    "\n",
    "If your widgets are not running properly in binder--jupyter lab is known to be problematic for versions of ipywidgets beyond 7.5--\n",
    "\n",
    "you can run pip list to see your version. \n",
    "\n",
    "Either downgrade or:\n",
    "\n",
    "\n",
    "delete ipywidgets\n",
    "\n",
    "pip install ipywidgets\n",
    "conda install -c conda-forge ipyml\n",
    "\n",
    "check if it worked by running:\n",
    "\n",
    "!jupyter labextension list\n",
    "\n",
    "If it works, you should be good to go!\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bff3ff8",
   "metadata": {},
   "source": [
    "## When running in jupyterlab and generating an error, try debugging with %debug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d1219713",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> \u001b[0;32m/Users/bonnymcclain/opt/miniconda3/envs/geo-env/lib/python3.9/site-packages/ee/main.py\u001b[0m(10)\u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m      8 \u001b[0;31m\u001b[0;32mimport\u001b[0m \u001b[0mstat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m      9 \u001b[0;31m\u001b[0;32mimport\u001b[0m \u001b[0mplistlib\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m---> 10 \u001b[0;31m\u001b[0;32mimport\u001b[0m \u001b[0mStringIO\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m     11 \u001b[0;31m\u001b[0;32mimport\u001b[0m \u001b[0mplatform\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m     12 \u001b[0;31m\u001b[0;32mimport\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "ipdb> h\n",
      "\n",
      "Documented commands (type help <topic>):\n",
      "========================================\n",
      "EOF    commands   enable    ll        pp       s                until \n",
      "a      condition  exit      longlist  psource  skip_hidden      up    \n",
      "alias  cont       h         n         q        skip_predicates  w     \n",
      "args   context    help      next      quit     source           whatis\n",
      "b      continue   ignore    p         r        step             where \n",
      "break  d          interact  pdef      restart  tbreak         \n",
      "bt     debug      j         pdoc      return   u              \n",
      "c      disable    jump      pfile     retval   unalias        \n",
      "cl     display    l         pinfo     run      undisplay      \n",
      "clear  down       list      pinfo2    rv       unt            \n",
      "\n",
      "Miscellaneous help topics:\n",
      "==========================\n",
      "exec  pdb\n",
      "\n",
      "ipdb> quit\n"
     ]
    }
   ],
   "source": [
    "# %debug"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d557abb",
   "metadata": {},
   "source": [
    "# Run the code cells below"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c60c95d1",
   "metadata": {},
   "source": [
    "Remember to Restart Kernel and Run All Cells from time to time to clear the memory and to rerun the cells in order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bgpfAEylAMX_",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bgpfAEylAMX_",
    "outputId": "30e5a541-71d3-441f-9337-54cc81b49689"
   },
   "outputs": [],
   "source": [
    " #!pip install geemap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1b4c542",
   "metadata": {},
   "source": [
    "If your imports stop working you might need to update the packages -- remove the # when you want to update"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75e7120a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install -U earthengine-api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f736f52b",
   "metadata": {
    "id": "f736f52b"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import geemap\n",
    "import ee\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85893b4d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 772,
     "referenced_widgets": [
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      "940c7a9d226e49bfb2aa0750784c0014",
      "f29dd9d36492494e838492c804d836b7",
      "f9269c4f6453436d8f92a2b2e720101a",
      "28f9d9094cad453f980715f8ad3245d1",
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      "7fac167d1f6d4fafac335bd1d4feda5b",
      "6e005aea2eea44a79f00e11f55172bc7"
     ]
    },
    "id": "85893b4d",
    "outputId": "d311d833-f479-460a-d783-56166d0f85b8"
   },
   "outputs": [],
   "source": [
    "map = geemap.Map(center=(40, -100), zoom=4)\n",
    "map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "38bd2b79",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 621,
     "referenced_widgets": [
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      "5d7c269c9617434f9002e485244a1a53",
      "67cb2be49e2b4f859188fac13b2f71fd",
      "8711dc6674a1470984bc852caa0ca3ca"
     ]
    },
    "id": "38bd2b79",
    "outputId": "8de09c3d-c811-44e5-a007-73235165b39e"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "71d1112e27494258bae82341b4ad5ae0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[40, -100], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=HBox(children=(T…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "map = geemap.Map(center =(40, -100), zoom= 4)\n",
    "map.basemap()\n",
    "map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "992161c1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 621,
     "referenced_widgets": [
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      "cdff05c71b934ea1b5606017e4049451",
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      "8276647652234c0a8b3d8d67240b7160",
      "5e77f37ff5ad401b87ec0b9e4d55dca6",
      "55231033c96b42ad95a37d91670fb1d2",
      "1dd3e5c1a4254699a95ac657842a74af",
      "df5aa2f43ca84107a7637cca51ceacc5",
      "55f0c9b617854a20b78b448cf442ff1e",
      "0becfd15ed6d404d8abfa175d0369b75",
      "dd156fb45c3a40cbafe7f8e85d1d5754",
      "a816a8515f8845f4a0097cd78e3f5101",
      "fc62e90f4cfb4689a55387e919380930",
      "0614119143b94d268fdfbfb98aec027c",
      "0665c35c4f434bceb94f1c0bae9eee48",
      "4568b4542c3244a2a0a328b12997c5c6",
      "0e2db6adbbcd49fd9f13985d9b7de49f",
      "e640c91c2f1f4599943b436821f848c5",
      "84efb0e7b6c34789a918a54f69ebb736",
      "56c54e079b6546cdb70217f72a66ee53",
      "d740ef6a1d3d43939f849ca0aa9fb5f0"
     ]
    },
    "id": "992161c1",
    "outputId": "d7fcb4e2-8d72-4bbb-de00-e57d048579e1"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aa72b57821f8462d9d390ac13bed74a8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[40, -100], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=HBox(children=(T…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "map = geemap.Map(center=(40, -100), zoom=4)\n",
    "map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aef252e2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aef252e2",
    "outputId": "749df445-af86-4540-9935-21b7015147fa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "144241\n"
     ]
    }
   ],
   "source": [
    "collection = ee.ImageCollection('LANDSAT/LC09/C02/T1_L2')\n",
    "print(collection.size().getInfo())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aa9f285c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aa9f285c",
    "outputId": "e73cb040-a954-4cf1-e020-c777891c7530"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1470530\n"
     ]
    }
   ],
   "source": [
    "collection = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')\n",
    "print(collection.size().getInfo())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "03fbabeb",
   "metadata": {
    "id": "03fbabeb"
   },
   "outputs": [],
   "source": [
    "def apply_scale_factors(image):\n",
    "    opticalBands = image.select('SR_B.*').multiply(0.0000275).add(-0.2)\n",
    "    thermalBands = image.select('ST_B.*').multiply(0.00341802).add(149.0)\n",
    "    return image.addBands(opticalBands, None, True).addBands(thermalBands, None, True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "18055bcb",
   "metadata": {
    "id": "18055bcb"
   },
   "outputs": [],
   "source": [
    "median =collection.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b63b099e",
   "metadata": {
    "id": "b63b099e"
   },
   "outputs": [],
   "source": [
    "dataset = apply_scale_factors(median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bb3bd7c5",
   "metadata": {
    "id": "bb3bd7c5"
   },
   "outputs": [],
   "source": [
    "vis_natural = {\n",
    "   'bands': ['SR_B4', 'SR_B3', 'SR_B2'],\n",
    "   'min': 0.0,\n",
    "   'max': 0.3,\n",
    "}\n",
    "\n",
    "vis_nir = {\n",
    "   'bands': ['SR_B5', 'SR_B4', 'SR_B3'],\n",
    "   'min': 0.0,\n",
    "   'max': 0.3,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ce01be27",
   "metadata": {
    "id": "ce01be27"
   },
   "outputs": [],
   "source": [
    "map.addLayer(dataset, vis_natural, 'True color (432)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b5b73d5d",
   "metadata": {
    "id": "b5b73d5d"
   },
   "outputs": [],
   "source": [
    "vis_params = [\n",
    "   {'bands': ['SR_B4', 'SR_B3', 'SR_B2'], 'min': 0, 'max': 0.3},\n",
    "   {'bands': ['SR_B5', 'SR_B4', 'SR_B3'], 'min': 0, 'max': 0.3},\n",
    "   {'bands': ['SR_B7', 'SR_B6', 'SR_B4'], 'min': 0, 'max': 0.3},\n",
    "   {'bands': ['SR_B6', 'SR_B5', 'SR_B2'], 'min': 0, 'max': 0.3},\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "783f9d13",
   "metadata": {
    "id": "783f9d13"
   },
   "outputs": [],
   "source": [
    "labels = [\n",
    "   'Natural Color (4, 3, 2)',\n",
    "   'Color Infrared (5, 4, 3)',\n",
    "   'Short-Wave Infrared (7, 6 4)',\n",
    "   'Agriculture (6, 5, 2)',\n",
    "]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35195329",
   "metadata": {
    "id": "35195329"
   },
   "source": [
    "This might take a few minutes..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6f038082",
   "metadata": {
    "colab": {
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      "900f75b614764bc6a365a82334a69171"
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    "id": "6f038082",
    "outputId": "a317376e-12ef-45ef-884a-622ab049b1a9"
   },
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    {
     "data": {
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       "model_id": "29fec40675b844479de46d9b38f52bbb",
       "version_major": 2,
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      "text/plain": [
       "GridspecLayout(children=(Output(layout=Layout(grid_area='widget001')), Output(layout=Layout(grid_area='widget0…"
      ]
     },
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    }
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    "geemap.linked_maps(\n",
    "   rows=2,\n",
    "   cols=2,\n",
    "   height=\"400px\",\n",
    "   center=[-3.4653, -62.2159],\n",
    "   zoom=4,\n",
    "   ee_objects=[dataset],\n",
    "   vis_params=vis_params,\n",
    "   labels=labels,\n",
    "   label_position=\"topright\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
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    {
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       "Map(center=[20, 0], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=HBox(children=(Togg…"
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    "map = geemap.Map()  \n",
    "map"
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    {
     "name": "stdout",
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     "text": [
      "Products: ee.List({\n",
      "  \"functionInvocationValue\": {\n",
      "    \"functionName\": \"AggregateFeatureCollection.array\",\n",
      "    \"arguments\": {\n",
      "      \"collection\": {\n",
      "        \"functionInvocationValue\": {\n",
      "          \"functionName\": \"ImageCollection.load\",\n",
      "          \"arguments\": {\n",
      "            \"id\": {\n",
      "              \"constantValue\": \"USGS/NLCD_RELEASES/2019_REL/NLCD\"\n",
      "            }\n",
      "          }\n",
      "        }\n",
      "      },\n",
      "      \"property\": {\n",
      "        \"constantValue\": \"system:index\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "})\n",
      "Bands: ['landcover', 'impervious', 'impervious_descriptor']\n"
     ]
    },
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       "Map(center=[38, -95], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=HBox(children=(To…"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "import geemap\n",
    "\n",
    "Map = geemap.Map()\n",
    "\n",
    "# Import the NLCD collection.\n",
    "dataset = ee.ImageCollection('USGS/NLCD_RELEASES/2019_REL/NLCD')\n",
    "\n",
    "# The collection contains images for multiple years and regions in the USA.\n",
    "print('Products:', dataset.aggregate_array('system:index'))\n",
    "\n",
    "# Filter the collection to the 2016 product.\n",
    "nlcd2016 = dataset.filter(ee.Filter.eq('system:index', '2016')).first()\n",
    "\n",
    "# Each product has multiple bands for describing aspects of land cover.\n",
    "print('Bands:', nlcd2016.bandNames().getInfo())\n",
    "\n",
    "# Select the land cover band.\n",
    "landcover = nlcd2016.select('landcover')\n",
    "\n",
    "# Display land cover on the map.\n",
    "Map.setCenter(-95, 38, 5)\n",
    "Map.addLayer(landcover, None, 'Landcover')\n",
    "Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "281775f5",
   "metadata": {
    "id": "281775f5"
   },
   "outputs": [],
   "source": [
    "dataset_xed = ee.ImageCollection('USGS/NLCD_RELEASES/2016_REL')\n",
    "Map.addLayer(dataset_xed, {}, \"USGS/NLCD_RELEASES/2016_REL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1b009a7f",
   "metadata": {
    "id": "1b009a7f"
   },
   "outputs": [],
   "source": [
    "dataset_hpn = ee.ImageCollection('USGS/NLCD_RELEASES/2016_REL')\n",
    "Map.addLayer(dataset_hpn, {}, \"USGS/NLCD_RELEASES/2016_REL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "730572f5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "730572f5",
    "outputId": "1ce17ce1-0ce7-4040-c0bb-2e4456548691"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NLCD\n",
      "ESA_WorldCover\n",
      "ESRI_LandCover\n",
      "ESRI_LandCover_TS\n",
      "Dynamic_World\n",
      "NWI\n",
      "MODIS/051/MCD12Q1\n",
      "MODIS/006/MCD12Q1\n",
      "GLOBCOVER\n",
      "JAXA/PALSAR\n",
      "Oxford\n",
      "AAFC/ACI\n",
      "COPERNICUS/CORINE/V20/100m\n",
      "COPERNICUS/Landcover/100m/Proba-V/Global\n",
      "USDA/NASS/CDL\n",
      "ALOS_landforms\n"
     ]
    }
   ],
   "source": [
    "legends = geemap.builtin_legends\n",
    "for legend in legends:\n",
    "    print(legend)\n",
    "\n"
   ]
  },
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    {
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       "model_id": "491c2e0646ef40f2aeec680f5d244327",
       "version_major": 2,
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      "text/plain": [
       "Map(center=[20, 0], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=HBox(children=(Togg…"
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   "source": [
    "Map = geemap.Map()\n",
    "\n",
    "legend_dict = {\n",
    "    '11 Open Water': '466b9f',\n",
    "    '12 Perennial Ice/Snow': 'd1def8',\n",
    "    '21 Developed, Open Space': 'dec5c5',\n",
    "    '22 Developed, Low Intensity': 'd99282',\n",
    "    '23 Developed, Medium Intensity': 'eb0000',\n",
    "    '24 Developed High Intensity': 'ab0000',\n",
    "    '31 Barren Land (Rock/Sand/Clay)': 'b3ac9f',\n",
    "    '41 Deciduous Forest': '68ab5f',\n",
    "    '42 Evergreen Forest': '1c5f2c',\n",
    "    '43 Mixed Forest': 'b5c58f',\n",
    "    '51 Dwarf Scrub': 'af963c',\n",
    "    '52 Shrub/Scrub': 'ccb879',\n",
    "    '71 Grassland/Herbaceous': 'dfdfc2',\n",
    "    '72 Sedge/Herbaceous': 'd1d182',\n",
    "    '73 Lichens': 'a3cc51',\n",
    "    '74 Moss': '82ba9e',\n",
    "    '81 Pasture/Hay': 'dcd939',\n",
    "    '82 Cultivated Crops': 'ab6c28',\n",
    "    '90 Woody Wetlands': 'b8d9eb',\n",
    "    '95 Emergent Herbaceous Wetlands': '6c9fb8',\n",
    "}\n",
    "\n",
    "landcover = ee.Image('USGS/NLCD/NLCD2016').select('landcover')\n",
    "Map.addLayer(landcover, {}, 'NLCD Land Cover')\n",
    "\n",
    "Map.add_legend(legend_title=\"NLCD Land Cover Classification\", legend_dict=legend_dict)\n",
    "Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
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    {
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       "Map(center=[20, 0], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=HBox(children=(Togg…"
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   ],
   "source": [
    "Map = geemap.Map()\n",
    "Map.add_basemap('HYBRID')\n",
    "landcover = ee.Image('USGS/NLCD_RELEASES/2019_REL/NLCD/2019').select('landcover')\n",
    "Map.addLayer(landcover, {}, 'NLCD Land Cover')\n",
    "Map.add_legend(builtin_legend='NLCD')\n",
    "Map"
   ]
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   "id": "rQb8clz4Cpy_",
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   "source": [
    "When sharing notebooks, it is always good to share dependencies...run pip list below:"
   ]
  }
 ],
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